Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT

Riemer H J A Slart^{1

2}, Michelle C Williams^{3

4}, Luis Eduardo Juarez-Orozco^{5

6}, Christoph Rischpler⁷, Marc R Dweck^{3

4}, Andor W J M Glaudemans⁸, Alessia Gimelli⁹, Panagiotis Georgoulias¹⁰, Olivier Gheysens¹¹, Oliver Gaemperli¹², Gilbert Habib^{13

14}, Roland Hustinx¹⁵, Bernard Cosyns¹⁶, Hein J Verberne¹⁷, Fabien Hyafil^{18

19}, Paola A Erba^{8

20

21}, Mark Lubberink^{22

23}, Piotr Slomka²⁴, Ivana Išgum^{17

25}, Dimitris Visvikis²⁶, Márton Kolossváry²⁷, Antti Saraste^{28

29}

Affiliations

¹ Medical Imaging Centre, Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands. r.h.j.a.slart@umcg.nl.
² Faculty of Science and Technology Biomedical, Photonic Imaging, University of Twente, Enschede, The Netherlands. r.h.j.a.slart@umcg.nl.
³ British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK.
⁴ Edinburgh Imaging facility QMRI, Edinburgh, UK.
⁵ Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
⁶ University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
⁷ Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.
⁸ Medical Imaging Centre, Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands.
⁹ Fondazione Toscana G. Monasterio, Pisa, Italy.
¹⁰ Department of Nuclear Medicine, Faculty of Medicine, University of Thessaly, University Hospital of Larissa, Larissa, Greece.
¹¹ Department of Nuclear Medicine, Cliniques Universitaires Saint-Luc and Institute of Clinical and Experimental Research (IREC), Université catholique de Louvain (UCLouvain), Brussels, Belgium.
¹² HeartClinic, Hirslanden Hospital Zurich, Zurich, Switzerland.
¹³ APHM, Cardiology Department, La Timone Hospital, Marseille, France.
¹⁴ IRD, APHM, MEPHI, IHU-Méditerranée Infection, Aix Marseille Université, Marseille, France.
¹⁵ Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, ULiège, Liège, Belgium.
¹⁶ Department of Cardiology, Centrum voor Hart en Vaatziekten, Universitair Ziekenhuis Brussel, 101 Laarbeeklaan, 1090, Brussels, Belgium.
¹⁷ Department of Radiology and Nuclear Medicine, Amsterdam UMC, location AMC, University of Amsterdam, Amsterdam, The Netherlands.
¹⁸ Department of Nuclear Medicine, DMU IMAGINA, Georges-Pompidou European Hospital, Assistance Publique - Hôpitaux de Paris, F-75015, Paris, France.
¹⁹ University of Paris, PARCC, INSERM, F-75006, Paris, France.
²⁰ Department of Nuclear Medicine (P.A.E.), University of Pisa, Pisa, Italy.
²¹ Department of Translational Research and New Technology in Medicine (P.A.E.), University of Pisa, Pisa, Italy.
²² Department of Surgical Sciences/Radiology, Uppsala University, Uppsala, Sweden.
²³ Medical Physics, Uppsala University Hospital, Uppsala, Sweden.
²⁴ Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
²⁵ Department of Biomedical Engineering and Physics, Amsterdam UMC - location AMC, University of Amsterdam, 1105, Amsterdam, AZ, Netherlands.
²⁶ INSERM, LaTIM, UMR 1101, Univ Brest, Brest, France.
²⁷ MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, 68 Városmajor Street, Budapest, Hungary.
²⁸ Turku PET Centre, Turku University Hospital, University of Turku, Turku, Finland.
²⁹ Heart Center, Turku University Hospital, Turku, Finland.

PMID: 33864509
PMCID: PMC8113178
DOI: 10.1007/s00259-021-05341-z

Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT

Riemer H J A Slart et al. Eur J Nucl Med Mol Imaging. 2021 May.

. 2021 May;48(5):1399-1413.

doi: 10.1007/s00259-021-05341-z. Epub 2021 Apr 17.

Authors

Affiliations

¹ Medical Imaging Centre, Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands. r.h.j.a.slart@umcg.nl.
² Faculty of Science and Technology Biomedical, Photonic Imaging, University of Twente, Enschede, The Netherlands. r.h.j.a.slart@umcg.nl.
³ British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK.
⁴ Edinburgh Imaging facility QMRI, Edinburgh, UK.
⁵ Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
⁶ University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
⁷ Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.
⁸ Medical Imaging Centre, Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO 9700 RB, Groningen, The Netherlands.
⁹ Fondazione Toscana G. Monasterio, Pisa, Italy.
¹⁰ Department of Nuclear Medicine, Faculty of Medicine, University of Thessaly, University Hospital of Larissa, Larissa, Greece.
¹¹ Department of Nuclear Medicine, Cliniques Universitaires Saint-Luc and Institute of Clinical and Experimental Research (IREC), Université catholique de Louvain (UCLouvain), Brussels, Belgium.
¹² HeartClinic, Hirslanden Hospital Zurich, Zurich, Switzerland.
¹³ APHM, Cardiology Department, La Timone Hospital, Marseille, France.
¹⁴ IRD, APHM, MEPHI, IHU-Méditerranée Infection, Aix Marseille Université, Marseille, France.
¹⁵ Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, ULiège, Liège, Belgium.
¹⁶ Department of Cardiology, Centrum voor Hart en Vaatziekten, Universitair Ziekenhuis Brussel, 101 Laarbeeklaan, 1090, Brussels, Belgium.
¹⁷ Department of Radiology and Nuclear Medicine, Amsterdam UMC, location AMC, University of Amsterdam, Amsterdam, The Netherlands.
¹⁸ Department of Nuclear Medicine, DMU IMAGINA, Georges-Pompidou European Hospital, Assistance Publique - Hôpitaux de Paris, F-75015, Paris, France.
¹⁹ University of Paris, PARCC, INSERM, F-75006, Paris, France.
²⁰ Department of Nuclear Medicine (P.A.E.), University of Pisa, Pisa, Italy.
²¹ Department of Translational Research and New Technology in Medicine (P.A.E.), University of Pisa, Pisa, Italy.
²² Department of Surgical Sciences/Radiology, Uppsala University, Uppsala, Sweden.
²³ Medical Physics, Uppsala University Hospital, Uppsala, Sweden.
²⁴ Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
²⁵ Department of Biomedical Engineering and Physics, Amsterdam UMC - location AMC, University of Amsterdam, 1105, Amsterdam, AZ, Netherlands.
²⁶ INSERM, LaTIM, UMR 1101, Univ Brest, Brest, France.
²⁷ MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, 68 Városmajor Street, Budapest, Hungary.
²⁸ Turku PET Centre, Turku University Hospital, University of Turku, Turku, Finland.
²⁹ Heart Center, Turku University Hospital, Turku, Finland.

PMID: 33864509
PMCID: PMC8113178
DOI: 10.1007/s00259-021-05341-z

Abstract

In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.

Keywords: Cardiovascular; Deep learning; Machine learning; Multimodality imaging; Position paper.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

**Fig. 1**
Conceptual Framework. Modified from Juarez-Orozco et al. [37]

**Fig. 2**
Artificial intelligence assisted image analysis. Artificial intelligence is currently fueled by machine learning algorithms, which can be roughly classified into: classical machine learning models and deep learning models. These can be used in a variety of imaging tasks, including pre-processing, image analysis, and image interpretation. ML machine learning

**Fig. 3**
Potential roles of AI in cardiac imaging. Depiction of an exemplary PET/CT case. Male with non-significant atherosclerosis in the left circumflex and overall preserved perfusion reserve in which DL-based processing of PET myocardial blood flow polar maps automatically suggested low-risk of events at a 1–2 years horizon. Transparency on the workflows represents AI implementations that were not used in this particular example, namely automatic calcium score quantification, CTA (FFR) analysis, and ICA analysis. AI, artificial intelligence; Ca, calcium; CAD, coronary artery disease; CTA, computed tomography angiography; ICA, invasive coronary angiography; MACE, major adverse cardiovascular events; PET, positron emission tomography

See this image and copyright information in PMC

References

1. Chan S, Bailey J, Ros PR. Artificial intelligence in radiology: summary of the AUR academic radiology and industry leaders roundtable. Acad Radiol. 2020;27(1):117–120. - PubMed
1. Géron A. Hands-on machine learnng with scikit-learn, Keras, and tensorflow: concepts, tools, and techniques to build intelligent systems. 2nd edition. Incorperated, editor. O’Reilly Media, USA; 2019.
1. Russell S, Norvig P. Artificial intelligence: a modern approach. 3rd edition. Pearson Education Limited; 2016.
1. Mitchell TM. Machine learning. McGraw Hill. 1997.
1. Kolossvary M, Kellermayer M, Merkely B, Maurovich-Horvat P. Cardiac computed tomography radiomics: a comprehensive review on radiomic techniques. J Thorac Imaging. 2018;33(1):26–34. - PubMed

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Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT

Affiliations

Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

Publication types

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources

Other Literature Sources